SHREC'14 - Shape Retrieval of Non-Rigid 3D Human Models

Introduction

The ability to recognise a deformable object's shape, regardless of the pose of
the object, is an important requirement for modern shape retrieval methods. We
have produced a new dataset for non-rigid 3D shape retrieval,
one that is much more challenging than previous datasets. Our dataset
features exclusively human models, in a variety of body shapes and poses. 3D
models of humans are commonly used within computer graphics and vision,
therefore the ability to distinguish between body shapes is an important
feature for shape retrieval methods. The shape differences between humans
are much more subtle than the differences between the shape classes used
in previous datasets, but humans are able to visually
recognise specific individuals. Successfully performing shape retrieval on a
dataset of human models is therefore a far more challenging, but realistic
task.

We welcome the submission of results of both general non-rigid shape retrieval
methods, and specialised methods which are specifically designed to work with
human models. We will evaluate general and specialised methods separately, to
produce a fairer comparison of results.

To participate in this track, please register your participation by emailing
d.pickup@cs.cf.ac.uk.

Paper

The results of the track have now been published in the 3DOR paper available to download here.

Dataset

Our track uses two datasets, and each participant is asked to produce
the results of their methods on both datasets individually. The first dataset
is made up of “real” data, obtained by scanning real human participants. The
second dataset is made up of “synthetic” data, created using DAZ Studio. The
different characteristics of these two datasets may each provide their own
unique challenges, and will ensure methods are not too finely tuned for just
one type of data.

The models in the dataset do contain self-intersections in some of the poses.
The models in the “real” dataset are closed, but the models in the
“synthetic” dataset contain a hole for each eye, and a hole inside
the mouth. Some of the meshes also contain a small number of high valence
vertices, or inward facing triangles. Participents are free to preprocess the models if their methods
can't deal with these properties. The file format of the models is
.obj.
We also provide a classification file (.cla) for each dataset
(the format of a .cla is explained here).

Real Dataset

The “real” human dataset is built from the point-clouds contained
within the Civilian American and European Surface Anthropometry Resource
(CAESAR).
The dataset is composed of 400 meshes, made up of 40 human
subjects in 10 different poses. Half the human subjects are male, and half
female. The point-cloud models were selected from CAESAR, and we employed
the SCAPE (shape completion and animation of people) method [1]
to build the 3D meshes, by fitting a template mesh to each subject. The
poses of each subjects are built by using a data-driven deformation
technique [2], which can produce realistic deformations of articulated meshes.
We have retriangulated the models using the freely available software by Valette
et al. (ACVD). This
retriangulation method uses smaller triangles in areas with higher levels of
detail. The size of the triangles is therefore not uniform.
The resulting models are made up of approximately 15,000
vertices.

Synthetic Dataset

We have used 3D modelling/animation software to create a dataset of syn-
thetic human models. The software we have used is
DAZ Studio, which
includes a parametrized human model, where the parameters control the
models body shape. The dataset consists of 15 different human models,
each with its own unique body shape. Five of these are male, five female,
and five child body shapes. Each of these models exist in 20 different poses,
resulting in a dataset of 300 models. The same poses have been used for
each body shape, and objects are considered as part of the same class if they
share the same body shape. The models are all retriangulated using the
same method used with the “real” dataset. The resulting models are
made up of approximately 60,000 vertices.

DAZ 3D have given us permission to distribute this data freely for research
purposes. The data must not be sold or used commercially.

Task

There are two different types of retrieval tasks we will assess:

Returning a list of all models, ranked correctly by shape similarity to
a query model.

Return a list of models that all share the same shape as the query
model.

For both exercises each model in the database should be used as a separate
query model. For the first exercise, for each query we ask the participants
to provide a list of all the other models in the dataset ordered by
their similarity to the query model. For the second exercise, for each query
the participants are asked to submit a list of all the models which they
classify as “the same shape” as the query model. The similarity of
the models should be judged based on body shape, not pose.

Participants may submit results for just one or both of the retrieval
tasks, depending on which task(s) their methods are designed to perform. We
encourage all participants to enter a result for both. All participants must
email all their results to d.pickup@cs.cf.ac.uk, along with a brief description
of the methods used, by 8th February 2014.

Participants
should produce results for both datasets described above separately.

Task 1 Submission Format

Participants should submit two files, each with all the query results for Task 1
for one of the datasets. The file should be named
surname_method_T1_dataset.txt, where
surname should be replaced with the first author's surname,
method with the name of the method, and dataset with the name
of the dataset (real or synthetic).

The models in each dataset are numbered from 0 to N-1, where
N is the number of models in the dataset. Line i of the results
file should contain the retrieval results of using model i as the query.
So line 0 should contain the results for model 0, etc. Each line of retrieval
results should list all the remaining N-1 models in the dataset, ordered
by their similarity to the query model. The order should be from most similar,
to least similar. The models should be separated by a space.

Task 2 Submission Format

Participants should submit two files, each with all the query results for Task 2
for one of the datasets. The file should be named
surname_method_T2_dataset.txt, where
surname should be replaced with the first author's surname,
method with the name of the method, and dataset with the name
of the dataset (real or synthetic).

The models in each dataset are numbered from 0 to N-1, where
N is the number of models in the dataset. Line i of the results
file should contain the retrieval results of using model i as the query.
So line 0 should contain the results for model 0, etc. Each line of retrieval
results should list all the remaining models in the dataset that are considered
"the same shape" as the query model. The models should be separated by a space.